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Multiagent Planning in Partially Observable Uncertain Worlds| old_uid | 13598 |
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| title | Multiagent Planning in Partially Observable Uncertain Worlds |
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| start_date | 2014/03/13 |
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| schedule | 14h |
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| online | no |
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| location_info | salle 25-26 |
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| summary | Coordinating the operation of a group of decision makers or agents in stochastic environments is a long-standing challenge in AI. Decision theory offers a normative framework for optimizing decisions under uncertainty. But due to computational complexity barriers, developing decision-theoretic planning algorithms for multiagent systems has been a serious challenge. We describe a range of new formal models and algorithms to tackle this problem. Exact algorithms shed light on the structure and complexity of the problem, but they have limited use as only tiny problems can be solved optimally. We describe a number of effective approximation techniques that use bounded memory, sampling, and randomization. These methods can produce high-quality results in a variety of application domains such as mobile robot coordination and sensor network management. We examine the performance of these algorithms and describe current research efforts to further improve their applicability and scalability. |
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| responsibles | El Fallah-Seghrouchni, Maudet, Moraitis |
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